





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_lowerCamelCase 					=      logging.get_logger(__name__)
logging.set_verbosity_info()






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				str     ):
		if "xprophetnet" in prophetnet_checkpoint_path:
				SCREAMING_SNAKE_CASE__								=   XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   XLMProphetNetForConditionalGeneration.from_pretrained(
				    UpperCamelCase__       , output_loading_info=UpperCamelCase__     )
		else:
				SCREAMING_SNAKE_CASE__								=   ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   ProphetNetForConditionalGeneration.from_pretrained(
				    UpperCamelCase__       , output_loading_info=UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   ["""key_proj""", """value_proj""", """query_proj"""]

		SCREAMING_SNAKE_CASE__								=   {
		    """self_attn""": """ngram_self_attn""",
		    """cross_attn""": """encoder_attn""",
		    """cross_attn_layer_norm""": """encoder_attn_layer_norm""",
		    """feed_forward_layer_norm""": """final_layer_norm""",
		    """feed_forward""": """""",
		    """intermediate""": """fc1""",
		    """output""": """fc2""",
		    """key_proj""": """k_proj""",
		    """query_proj""": """q_proj""",
		    """value_proj""": """v_proj""",
		    """word_embeddings""": """embed_tokens""",
		    """embeddings_layer_norm""": """emb_layer_norm""",
		    """relative_pos_embeddings""": """relative_linear""",
		    """ngram_embeddings""": """ngram_input_embed""",
		    """position_embeddings""": """embed_positions""",
		}

		for key in loading_info["missing_keys"]:
				SCREAMING_SNAKE_CASE__								=   key.split("""."""     )

				if attributes[0] == "lm_head":
						SCREAMING_SNAKE_CASE__								=   prophet
						SCREAMING_SNAKE_CASE__								=   prophet_old
				else:
						SCREAMING_SNAKE_CASE__								=   prophet.prophetnet
						SCREAMING_SNAKE_CASE__								=   prophet_old.model

				SCREAMING_SNAKE_CASE__								=   False
				for attribute in attributes:
						if attribute in mapping:
								SCREAMING_SNAKE_CASE__								=   mapping[attribute]
								if not hasattr(UpperCamelCase__       , UpperCamelCase__     ) and len(UpperCamelCase__     ) > 0:
										SCREAMING_SNAKE_CASE__								=   attribute
						elif hasattr(UpperCamelCase__       , UpperCamelCase__     ):
								SCREAMING_SNAKE_CASE__								=   attribute

						if attribute == "weight":
								assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
								SCREAMING_SNAKE_CASE__								=   old_model.weight
								logger.info(f'''{attribute} is initialized.'''     )
								SCREAMING_SNAKE_CASE__								=   True
								break
						elif attribute == "bias":
								assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
								SCREAMING_SNAKE_CASE__								=   old_model.bias
								logger.info(f'''{attribute} is initialized'''     )
								SCREAMING_SNAKE_CASE__								=   True
								break
						elif attribute in special_keys and hasattr(UpperCamelCase__       , """in_proj_weight"""     ):
								SCREAMING_SNAKE_CASE__								=   old_model.in_proj_weight.shape[0] // 3
								SCREAMING_SNAKE_CASE__								=   getattr(UpperCamelCase__       , UpperCamelCase__     )
								param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
								param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
								if attribute == "query_proj":
										SCREAMING_SNAKE_CASE__								=   nn.Parameter(old_model.in_proj_weight[:embed_dim, :]     )
										SCREAMING_SNAKE_CASE__								=   nn.Parameter(old_model.in_proj_bias[:embed_dim]     )

								elif attribute == "key_proj":
										SCREAMING_SNAKE_CASE__								=   nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]     )
										SCREAMING_SNAKE_CASE__								=   nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]     )
								elif attribute == "value_proj":
										SCREAMING_SNAKE_CASE__								=   nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]     )
										SCREAMING_SNAKE_CASE__								=   nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]     )
								SCREAMING_SNAKE_CASE__								=   True
								break
						elif attribute == "position_embeddings":
								assert (
								    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
								), "Hidden size has to match"
								assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
								SCREAMING_SNAKE_CASE__								=   nn.Parameter(old_model.embed_positions.weight[:512, :]     )
								SCREAMING_SNAKE_CASE__								=   True
								break

						if attribute.isdigit():
								SCREAMING_SNAKE_CASE__								=   model[int(UpperCamelCase__     )]
								SCREAMING_SNAKE_CASE__								=   old_model[int(UpperCamelCase__     )]
						else:
								SCREAMING_SNAKE_CASE__								=   getattr(UpperCamelCase__       , UpperCamelCase__     )

								if old_attribute == "":
										SCREAMING_SNAKE_CASE__								=   old_model
								else:
										if not hasattr(UpperCamelCase__       , UpperCamelCase__     ):
												raise ValueError(f'''{old_model} does not have {old_attribute}'''     )
										SCREAMING_SNAKE_CASE__								=   getattr(UpperCamelCase__       , UpperCamelCase__     )

				if not is_key_init:
						raise ValueError(f'''{key} was not correctly initialized!'''     )

		print(f'''Saving model to {pytorch_dump_folder_path}'''     )
		prophet.save_pretrained(UpperCamelCase__     )


if __name__ == "__main__":
	_lowerCamelCase 					=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	_lowerCamelCase 					=      parser.parse_args()
	convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''


from __future__ import annotations







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if len(SCREAMING_SNAKE_CASE__	) <= 1 or n <= 1:
								return

				insert_next(SCREAMING_SNAKE_CASE__				, n - 1	)
				rec_insertion_sort(SCREAMING_SNAKE_CASE__				, n - 1	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if index >= len(SCREAMING_SNAKE_CASE__	) or collection[index - 1] <= collection[index]:
								return

				# Swaps adjacent elements since they are not in ascending order
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= (
				    collection[index],
				    collection[index - 1],
				)

				insert_next(SCREAMING_SNAKE_CASE__				, index + 1	)


if __name__ == "__main__":
				UpperCAmelCase_						:  List[str]				  =						input('Enter integers separated by spaces: ')
				UpperCAmelCase_						:  list[int]				  =						[int(num) for num in numbers.split()]
				rec_insertion_sort(number_list, len(number_list))
				print(number_list)




